# Copyright (c) 2018 Presto Labs Pte. Ltd.
# Author: jaewon

import datetime
import functools

from coin.base.datetime_util import to_datetime

from coin.exchange.binance.kr_rest.product import BinanceProduct
from coin.exchange.bitfinex_v2.kr_rest.product import BitfinexProduct
from coin.exchange.bitflyer_v1.kr_rest.futures_product import BitflyerFuturesProduct
from coin.exchange.bitmex.kr_rest.futures_product import BitmexFuturesProduct
from coin.exchange.gdax.kr_rest.product import GdaxProduct
from coin.exchange.okex_futures.kr_rest.futures_product import OkexFuturesProduct

from coin.strategy.mm.simple_sim import result_util
from coin.strategy.mm.simple_sim.strategy.pass_unhedge_3 import PassUnhedgedSimStrategy

ref_product = None
ref_product_true_book_funcs = None
trade_product = None
trade_product_true_book_funcs = None
price_multiplier_1 = None


def prepare(args, ref_ts):
  global ref_product, ref_product_true_book_funcs
  global trade_product, trade_product_true_book_funcs
  global price_multiplier_1

  trade_product = BitmexFuturesProduct.FromStr('ETH-USD.PERPETUAL')
  trade_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(500.)[1][-1][0]),
                                   (lambda book: book.get_notional_bids_by_qty(500.)[1][-1][0]))

  args = dict(enumerate(args))
  ref_exchange = args.get(0, 'Okex')
  if ref_exchange == 'Okex':
    ref_product = OkexFuturesProduct.FromStr('ETH-USD.QUARTER', current_datetime=ref_ts)
    ref_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(500.)[1][-1][0]),
                                   (lambda book: book.get_notional_bids_by_qty(500.)[1][-1][0]))

  elif ref_exchange == 'Binance':
    ref_product = BinanceProduct.FromStr('ETH-USDT')
    ref_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(20.)[1][-1][0]),
                                   (lambda book: book.get_notional_bids_by_qty(20.)[1][-1][0]))

  elif ref_exchange == 'Bitfinex':
    ref_product = BitfinexProduct.FromStr('ETH-USD')
    ref_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(20.)[1][-1][0]),
                                   (lambda book: book.get_notional_bids_by_qty(20.)[1][-1][0]))

  elif ref_exchange == 'Gdax':
    ref_product = GdaxProduct.FromStr('ETH-USD')
    ref_product_true_book_funcs = ((lambda book: book.get_notional_asks_by_qty(20.)[1][-1][0]),
                                   (lambda book: book.get_notional_bids_by_qty(20.)[1][-1][0]))

  else:
    raise ValueError('Unsupported: %s' % ref_exchange)


def get_products(from_ts):
  return [ref_product, trade_product]


def get_machines():
  return ['feed-01.eu-west-1.aws']


def get_time_ranges():
  ranges = []
  cur_dt = datetime.datetime(2018, 9, 15, 0, 0, 0)
  end_dt = datetime.datetime(2018, 10, 2, 0, 0, 0)
  while cur_dt < end_dt:
    ranges.append((cur_dt, cur_dt + datetime.timedelta(hours=24.5)))
    cur_dt += datetime.timedelta(hours=24)
  return ranges


def linear_sell_edge(edge, close_edge, max_pos, pos):
  if pos <= 0.:
    return edge
  elif pos >= max_pos:
    return close_edge
  else:
    p = (pos / float(max_pos))
    return (p * close_edge + (1 - p) * edge)


def linear_buy_edge(edge, close_edge, max_pos, pos):
  if pos >= 0.:
    return edge
  elif pos <= -max_pos:
    return close_edge
  else:
    p = (-pos / float(max_pos))
    return (p * close_edge + (1 - p) * edge)


def get_strategy(from_ts, to_ts):
  NS_PER_SECOND = (10**9)
  NS_PER_MINUTE = 60 * NS_PER_SECOND

  strategy_list = []

  for basis_ma_window in [2, 5, 10, 15, 20, 30, 45, 60]:
    for edge_bp in [2, 3, 4, 5, 6, 7, 9, 11, 13]:
      for close_edge_bp in [edge_bp]:  # [2, edge_bp]:
        for agg_edge in [None]:  # [None, (edge_bp + 8.5) / 10000.]:
          for stack in [6]:  # , 12, 18, 24]:
            lot_size = 40. / stack
            delay = 1.

            pricing_param = {
                'basis_ma_window': basis_ma_window * NS_PER_MINUTE,
                'tick': 0.05,
                'book_askt_func_1': ref_product_true_book_funcs[0],
                'book_bidt_func_1': ref_product_true_book_funcs[1],
                'book_askt_func_2': trade_product_true_book_funcs[0],
                'book_bidt_func_2': trade_product_true_book_funcs[1],
                'price_multiplier_1': price_multiplier_1
            }

            executor_param = {
                'lot_size': lot_size,
                'min_pos': -lot_size * stack,
                'max_pos': lot_size * stack,
                'maker_fee': -2.5 / 10000.,
                'taker_fee': 7.5 / 10000.,
                'execution_delay': delay * NS_PER_SECOND,
                'post_only': True,
                'trade_qty_func': (lambda trade: 6. * trade.qty / trade.price)
            }

            use_agg = 'without_agg'
            if agg_edge:
              use_agg = 'with_agg'
            name = '%02dm.%02dbp.%02dbp.%02dstack.%s.%s' % (basis_ma_window,
                                                            edge_bp,
                                                            close_edge_bp,
                                                            stack,
                                                            use_agg,
                                                            to_datetime(from_ts).strftime('%Y%m%d'))

            strategy = PassUnhedgedSimStrategy(trade_product,
                                               ref_product,
                                               functools.partial(linear_sell_edge,
                                                                 edge_bp / 10000.,
                                                                 close_edge_bp / 10000.,
                                                                 1.),
                                               functools.partial(linear_buy_edge,
                                                                 edge_bp / 10000.,
                                                                 close_edge_bp / 10000.,
                                                                 1.),
                                               pricing_param,
                                               executor_param,
                                               trade_after=basis_ma_window,
                                               name=name,
                                               agg_edge=agg_edge)
            #    feature_filepath=('out/feature.%s.csv' % name),
            #    fill_filepath=('out/fill.%s.csv' % name))
            strategy_list.append(strategy)

  return strategy_list


def get_strategy_result(strategy):
  return {'name': strategy.name, **strategy.get_summary()}


def aggregate_result(results):
  return result_util.aggregate_sim_result(results)
